Reciprocal Space Mapping

Area detector exposures from the pilatus3_100K or Merlin area detectors can be re-mapped onto a reciprocal-space Cartesian grid, allowing 3D volumes of h,k,l space to be plotted and new slices to be generated.

Re-mapped recirpocal space volumes can be used to determine peak-widths in natural units and allows visualisation of diffuse scattering and strain in Bragg peaks.

The re-mapping can be performed automatically at the end of each scan, or the scans can be re-processed later using a simple command line tool (or a new python GUI).

$Diffuse diffraction data capture by Pilatus3_100k area detector$

Diffuse diffraction data capture by Pilatus3_100k area detector

Detector images re-mapped onto a voxel-grid in the sample's reciprocal space.

Diamond's DAWN software used to visualise re-mapped diffuse scattering data

Example of a Bragg peak at a phase transition showing splitting

Calibration

The Pilatus3_100K and Merlin detectors are both fixed in place and their pixel positions have been calibrated vs the direct beam. This calibration, along with the diffractometer angles and the sample orientation matrix are stored in the NeXus scan files. These files are all that is required to perform the re-mapping procedure.

Normalisation

Normalisation of pixels-per-voxel is performed ensuring the correct intensity is assigned to each voxel. Each voxel is normalised by the volume of reciprocal space it suptends. Additional normalisation can be performed against the monitor, right current and beam polarisation.

Usage - Beamline

To run a scan with automatic msmapper from GDA:

>>> scancn eta 0.01 101 msmapper pil 1 roi1 roi2

This creates a hkl remapped file in the mm12345-1/processed directory with automatic volume and voxel step 0.01, then runs an autoprocssor jupyter notebook to analyse the remapped file. Result are visible in ispyb.

To run msmapper on previously completed scans from an I16 workstation:

$ module load msmapper

$ rs_map -s 0.002 --monitor rc -o /dls/i16/data/2024/mm12345-1/processing/12345_remap.nxs /dls/i16/data/2024/mm12345-1/12345.nxs

or use the cluster:

$ module load msmapper

$ i16_cluster_submit rs_map -s 0.002 -O Volume_HKL -o /dls/i16/data/2024/mm12345-1/processing/12345_remap.nxs --cores 4 --memory 2G /dls/i16/data/2024/mm12345-1/12345.nxs

To run a jupyter notebook to load and analyse a remapped scan (after msmapper has been performed)

$ module load python

$ python /dls_sw/i16/software/python/jupyter_processor/jproc.py mapper /dls/i16/data/2024/mm12345-1/12345.nxs

>> creates mm12345-1/processing/12345_remap.html

To reprocess the data using a python gui:

$ module load msmapper/1.8

$ python -m pip install --upgrade git+https://github.com/DiamondLightSource/i16_msmapper.git

$ python -m i16_msmapper

More Information and Examples

More information about MSMapper can be found on the i16_msmapper GitHub page, including instructions on installing MSMapper and the python wrapper locally (currently in testing).

https://github.com/DiamondLightSource/i16_msmapper

There is also more information on the beamline Confluence page (within Diamond only):

https://confluence.diamond.ac.uk/display/I16/HKL+Mapping

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